"About this title" may belong to another edition of this title. In addition, it could be particularly useful for molecular biologists" Theoretical Population Biology The course focuses on understanding and implementing methods for analysis of biological sequences, beyond pairwise alignment, and for prediction and. Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. The sequence alignment problem is concerned with finding the best alignment between two sequences that have evolved from a common ancestor by means of a series. My overall evaluation is that this book is very good and a must read for active participants in the field. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. "This is one of the more rewarding books I have read within this field. ".successfully integrates numerous probabilistic models with computational algorithms to solve molecular biology problems of sequence alignment.an excellent textbook selection for a course on bioinformatics and a very useful consultation book for a mathematician, statistician, or biometrician working in sequence alignment." Bulletin of Mathematical Biology In this thesis I present the work I have done. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of. Notably, the problem set includes all of the problems offered in Biological Sequence Analysis (BSA), by Durbin et al., widely adopted as a required text for. Biological sequences such as DNA, RNA and proteins can be represented as a sequence or string of characters. "The book is amply illustrated with biological applications and examples." Cell The field between computational science and biology is varyingly described as computational biology or bioinformatics. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. This book provides the first unified, up-to-date and self-contained account of such methods, and more generally of probabilistic methods of sequence analysis, presented in a Bayesian framework. Bioinformatics helps us understand complex biological problems by investigating similarities and differences that exist at sequence. The first section provides an overview of biological sequences (nucleic acids and proteins).
Probabilistic methods are assuming greater significance in the analysis of nucleotide sequence data. This chapter focuses on several biological sequence analysis techniques used in computational biology and bioinformatics.
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